### Abstract

Time-resolved Particle Image Velocimetry (PIV, 10 kHz) was used to measure the flow-field of a Mach 0.6 axisymmetric jet. The field was decomposed into its requisite spatial POD eigenfunctions, and the time dependent coefficients were recovered. An Artificial Neural Network (ANN) and a Linear Stochastic Estimation (LSE) model were then trained to estimate the first five time-dependent POD coefficients from five point velocity measurements made by”virtual crosswires” in the mixing layer. We show that the prediction accuracy is strongly dependent on the POD mode number for both models. On average, the ANN-based model is able to predict the velocity fluctuations more accurately than the LSE-based model. Finally, we examine the estimated reduced-order velocity fields and their correlation to analytically reconstructed reduced-order velocity fields. Possible extensions of this method are also discussed.

Original language | English (US) |
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State | Published - Jan 1 2019 |

Event | 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 - Southampton, United Kingdom Duration: Jul 30 2019 → Aug 2 2019 |

### Conference

Conference | 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 |
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Country | United Kingdom |

City | Southampton |

Period | 7/30/19 → 8/2/19 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Atmospheric Science
- Aerospace Engineering

### Cite this

*Velocity estimation in the mixing layer of a subsonic jet using artificial neural networks*. Paper presented at 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, Southampton, United Kingdom.

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**Velocity estimation in the mixing layer of a subsonic jet using artificial neural networks.** / Tenney, Andrew S.; Glauser, Mark N.; Berger, Zachary P.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Velocity estimation in the mixing layer of a subsonic jet using artificial neural networks

AU - Tenney, Andrew S.

AU - Glauser, Mark N.

AU - Berger, Zachary P.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Time-resolved Particle Image Velocimetry (PIV, 10 kHz) was used to measure the flow-field of a Mach 0.6 axisymmetric jet. The field was decomposed into its requisite spatial POD eigenfunctions, and the time dependent coefficients were recovered. An Artificial Neural Network (ANN) and a Linear Stochastic Estimation (LSE) model were then trained to estimate the first five time-dependent POD coefficients from five point velocity measurements made by”virtual crosswires” in the mixing layer. We show that the prediction accuracy is strongly dependent on the POD mode number for both models. On average, the ANN-based model is able to predict the velocity fluctuations more accurately than the LSE-based model. Finally, we examine the estimated reduced-order velocity fields and their correlation to analytically reconstructed reduced-order velocity fields. Possible extensions of this method are also discussed.

AB - Time-resolved Particle Image Velocimetry (PIV, 10 kHz) was used to measure the flow-field of a Mach 0.6 axisymmetric jet. The field was decomposed into its requisite spatial POD eigenfunctions, and the time dependent coefficients were recovered. An Artificial Neural Network (ANN) and a Linear Stochastic Estimation (LSE) model were then trained to estimate the first five time-dependent POD coefficients from five point velocity measurements made by”virtual crosswires” in the mixing layer. We show that the prediction accuracy is strongly dependent on the POD mode number for both models. On average, the ANN-based model is able to predict the velocity fluctuations more accurately than the LSE-based model. Finally, we examine the estimated reduced-order velocity fields and their correlation to analytically reconstructed reduced-order velocity fields. Possible extensions of this method are also discussed.

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M3 - Paper

AN - SCOPUS:85073635367

ER -